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Currently, glucose monitors typically include a clock function that allows the device to mark each individual glucose measurement with a timestamp. As the user tests with the device over a period of time, the time data collected and the associated glucose values are used by the patient and their care givers to create charts of glucose measurements. This set of data can be used to adjust patient medication or as a tool to gauge effectiveness of current treatments.
A flaw in current systems is that they are dependent on the users to correctly set the time. Setting the time on a glucose meter is not always a straight forward task and many of the users of glucose meters are older and/or not technologically savvy. It is also very likely that many users of glucose meters are not even aware that they should be setting the time on their devices. The difficulty in correctly setting the time results in many glucose meters collecting inaccurate data that is not useable by patient or caregiver.
A problem with episodic glucose monitoring is that patients only get a snapshot of their current blood glucose. They must make treatment decisions using that snapshot, rather than using dynamic information that more accurately reflects their condition.
For example, if a patient tests and finds that their blood glucose is low, they may decide to take in some carbohydrates to raise their blood sugar. However, it may be that their glucose is already rising, in which case they may not need as much, or any carbohydrates.
Conversely, a patient may test and find that their blood glucose is at a desirable level, not realizing that their glucose is actually rapidly dropping. In this case they will probably decide not to intake any carbohydrates, when in reality they may face a hypoglycemic event soon if they don't act.
It would be desirable for the glucose meter to be able to forecast what direction and how quickly glucose levels are changing. This would allow the user to more accurately treat themselves, thus reducing the likelihood of hypoglycemic or hyperglycemic events.
In order to do such forecasting, at least two things are required: enough historical data in order to predict future trends; and accurate time-stamping information associated with those historical glucose results.